## Step 1: Define the symbolic representation of the variables
The variables are 'milligrams of vitamin B7' and 'milligrams of vitamin B9', which we can denote as $x_1$ and $x_2$ respectively.

## Step 2: Define the attributes of the variables
The attributes or resources are given as:
- $r_0$: energy stability index
- $r_1$: kidney support index
- $r_2$: cognitive performance index
With specific values for $x_1$ and $x_2$:
- $r_0$: $x_0 = 11$, $x_1 = 14$
- $r_1$: $x_0 = 12$, $x_1 = 16$
- $r_2$: $x_0 = 12$, $x_1 = 4$

## 3: Formulate the objective function
The objective function to maximize is $4.43x_1 + 3.0x_2$.

## 4: Formulate the constraints
1. $11x_1 \leq 146$
2. $12x_1 \leq 162$
3. $12x_1 \leq 139$
4. $14x_2 \leq 146$
5. $16x_2 \leq 162$
6. $4x_2 \leq 139$
7. $11x_1 + 14x_2 \geq 49$
8. $12x_1 + 16x_2 \geq 61$
9. $12x_1 + 4x_2 \geq 47$
10. $-5x_1 + 2x_2 \geq 0$
11. $11x_1 + 14x_2 \leq 130$
12. $12x_1 + 16x_2 \leq 118$
13. $12x_1 + 4x_2 \leq 129$

## 5: Symbolic Representation
```json
{
'sym_variables': [('x1', 'milligrams of vitamin B7'), ('x2', 'milligrams of vitamin B9')],
'objective_function': '4.43*x1 + 3.0*x2',
'constraints': [
    '11*x1 <= 146',
    '12*x1 <= 162',
    '12*x1 <= 139',
    '14*x2 <= 146',
    '16*x2 <= 162',
    '4*x2 <= 139',
    '11*x1 + 14*x2 >= 49',
    '12*x1 + 16*x2 >= 61',
    '12*x1 + 4*x2 >= 47',
    '-5*x1 + 2*x2 >= 0',
    '11*x1 + 14*x2 <= 130',
    '12*x1 + 16*x2 <= 118',
    '12*x1 + 4*x2 <= 129'
]
}
```

## 6: Gurobi Code
```python
import gurobi

def solve_optimization_problem():
    # Create a new model
    model = gurobi.Model()

    # Define variables
    x1 = model.addVar(name="milligrams of vitamin B7", lb=0)
    x2 = model.addVar(name="milligrams of vitamin B9", lb=0)

    # Objective function
    model.setObjective(4.43 * x1 + 3.0 * x2, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(11 * x1 <= 146)
    model.addConstr(12 * x1 <= 162)
    model.addConstr(12 * x1 <= 139)
    model.addConstr(14 * x2 <= 146)
    model.addConstr(16 * x2 <= 162)
    model.addConstr(4 * x2 <= 139)
    model.addConstr(11 * x1 + 14 * x2 >= 49)
    model.addConstr(12 * x1 + 16 * x2 >= 61)
    model.addConstr(12 * x1 + 4 * x2 >= 47)
    model.addConstr(-5 * x1 + 2 * x2 >= 0)
    model.addConstr(11 * x1 + 14 * x2 <= 130)
    model.addConstr(12 * x1 + 16 * x2 <= 118)
    model.addConstr(12 * x1 + 4 * x2 <= 129)

    # Optimize
    model.optimize()

    # Print solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Milligrams of vitamin B7: {x1.varValue}")
        print(f"Milligrams of vitamin B9: {x2.varValue}")
        print(f"Objective function value: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```